Information Filtering System Based on Clustering Approach

Information Filtering System Based on Clustering Approach A PRIVATE NEIGHBOURHOOD BASED INFORMATION FILTERING SYSTEM BASED ON CLUSTERING APPROACH ABSTRACT The quantity of web information has been increased day by day due to fast development of internet. Now-a-days people make their decision based on the available information from the internet. But the problem is how the people successfully choose or filter the useful information from the enormous amount of information. This problem is referred as information overload. Recommendation System is a supportive tool to resolve the information overload problem. It is part of information filtering system used to recommend the user based on their own interest, neighborhood similarity and past history. Collaborative Filtering is one of the popular techniques widely used recommendation system. Every recommendation system should ensure privacy for both user’s neighbour and their data. To overcome the scalability and model reconstruction problem, a power graph based private neighborhood recommendation system is proposed to ensure the user’s privacy. First, the compressed network is constructed and then the feature set is extracted from the compressed network using transformed data. The data is transformed using hybrid transformation fuses principal component analysis and rotation transformation to protect users privacy with accurate recommendations. Finally the item to be recommended is predicted which achieve better performance than the existing technique. MovieLens Dataset is used to evaluate this method. INTRODUCTION Recommendation System is one of the information filtering system which provides valuable information to the users by filtering the information according to user’s interest. Traditional approaches of recommendation systems are collaborative filtering, content based filtering and hybrid Approach. Content Based Filtering (CBF) approach predicts the recommendation based on the rating given by the user for the similar items in past history. Collaborative Filtering (CF) recommends the user based on rating of that item by similar users. Hybrid approach combines both the approaches. All the approaches have their own advantage and disadvantage. CF mainly classified as memory based CF and model based CF. Memory based CF first calculate the similarities between the requested user and all other user to find the neighbors then calculate the prediction based on identified neighbors rating pattern. Model based method first built a model based on the preference of the user. Main aim of the recommender system is to minimize the prediction error. The main issues in CF recommender system are scalability, sparsity and privacy. Scalability: Large number of users and items in the network led to the increase in the computational complexity of the system. In E-commerce, scalability plays a important issue because it contain huge number of users. Sparsity: All the users dont show their interest to rate all the items they interact private, which will lead to data sparseness in the system. This will not give exact recommendation to the seekers. Cold Start: Lack of information for new items and users in recommendation system will leads to unpredictable items in the system. Privacy: Users may provide false information inorder to protect their personal information. This leads to inaccurate recommendation. The proposed work mainly focuses on two fundamental issues in CF namely scalability and privacy. The first challenge is how to improve the scalability of CF, because these systems should search the entire user for finding the neighbors. The second problem is how to protect the individual users privacy while prediction. Both an issues lead to poor performance of the system. So the important challenge is to handle both a situation properly for better performance. LITERATURE SURVEY Recommendation system helps the people to get exact information based on neighbors’ pattern. Remarkable growth in e-commerce site makes the online vendors to develop their sales and profits. They use this technique which suggests product to users’ by their neighbors’ preference about the item. Scalability issue in RS mainly due to enormous growth in users tends to decline in accuracy of prediction on recommendation. Clustering approach reduces scalability problem by grouping the similar users. Recommender System may demand the users’ to expose their ratings to recommendation server to give a proper recommendation. But exposing the rating may allow the recommenders to learn the private information about users. Revealing rating may also direct to do violent behavior by several competitive companies’. CLUSTERING IN RECOMMENDER SYSTEM Several different clustering methods are adapted to reduce the scalability problem in RS. A new cluster based matrix tri-factorization is proposed to cluster the user and item simultaneously to get a better recommendation in model based CF. But when the new user enter the system it is necessary to rebuilt the whole model again for other user [].In [0] a cluster based binary tree is built by splitting the dataset and the recommendation is predicted based on the average rating of cluster. Later [] a combined k-means bisecting clustering is performed to overcome the scalability problem while preprocessing and pseudo prediction is adapted. But performance is not much better. Community based clustering model based CF is proposed [] to predict the recommendation but it underperform on outliers. Multilevel clustering is adapted to extract the subgraph which is clustered and propagated to reduce scalability which improved the performance than existing approach. But it will be more complicate d when the aspect of the network increases. Therefore it is necessary to group the data in all the aspects to reduce the scalability. PRIVACY PRESERVING RECOMMENDER SYSTEM In CF, neighbors are identified by collecting the information for the entire user. Thus the server maintain user preference, purchase, usage data etc which may contain identifiable information may violate the privacy. There are several techniques to protect the user’s sensitive information []. Initial method to ensure the privacy protection in CF was proposed by canny (2002a, 2002b), mainly focus on aggregation. In this method sensitive data are aggregated with some common distribution. In cryptographic approach, Individual user data can be protected using homomorphic encryption to avoid exposing of individual data but it requires high computational cost [5]. In perturbation approach, users mask their data before storing it in a central server. The central server collects the disguised data instead of original data to provide predictions with decent accuracy [18]. In [2] a randomized response techniques (RRT) is proposed to preserve users’ privacy by generating naive Ba yesian classifier (NBC) based private recommendations. Another technique, data obfuscation was used to implement privacy preserving collaborative filtering algorithm [16]. In this technique, sensitive data are obfuscated through additive or multiplicative noise in order to protect individual privacy before allowing for analysis. The actual data can be revealed in this technique by applying reverse engineering process [7]. Sensitive information is either concealed or eliminated for the purpose of analyzing the data to extract the knowledge in anonymization technique. The major fault of this technique is some distinctive data may lead to the re-identification of data [1]. In proposed work, a scalable privacy preserving recommendation system is proposed. First the user to user network is constructed from the user preference then compressed network is formed based on the power graph approach. Then feature set extracted from the compressed network based on transformed rating to ensure the privacy during prediction. Finally the linear prediction model is adopted instead of similarity prediction to improve the accuracy besides reduces the complexity. OBJECTIVE To protect the individual’s neighbour information while prediction based on clustering approach this reduces complexity of model reconstruction. To protect the individual data using data transformation technique. PROBLEM FORMULATION A cluster based approach is proposed to protect the individual neighborhood privacy and hybrid data transformation technique is proposed to protect the individual data with accurate recommendation using feature extraction based linear regression prediction. MODULES Data Transformation Experiment is performed using MovieLens Public (MLP) dataset which is the standard dataset to show the better performance of the proposed method. MovieLens dataset is collected by the GroupLens Research Project at the University of Minnesota. This data set consists of three different files of three different sizes 10M, 1M and 10K which mainly contain ratings of different movies provided by the users. To evaluate the proposed method 1M size dataset is used which contains 6040 users, 1 million ratings and 3900 items. The rating values are on five star scales, with five stars being the best and one star being the least. Data collected consist of four attributes separated with double colon as the delimiter [userid :: itemid ::rating :: Timestamp]. To evaluate the proposed work userid, itemid, rating is extracted from the dataset and then extracted data is converted into user x item matrix with dimension (6040 x 3952).Unrated items are filled with value zero to overcome computation comple xity. Data Transformation A hybrid data transformation technique which fuses Principal Component Analysis (PCA) and Rotation Transformation (RT) is proposed to transform the data in order to protect the user’s data. The input to the PCA technique is the rating matrix. This technique first finds the principal components and then rotates these components which cannot be reverted easily. Rotation transformation will be efficient by identifying the appropriate range of angle such that to satisfy the least privacy requirement. Optimum privacy threshold is determined from range of angle which leads to good privacy protection effect. To determine the range of angles, sequence rotation should be performed on vectors with successive angles. For each pair of attributes, pairwise optimum privacy threshold is assigned by multiplying the privacy threshold and the privacy angle which should be maximum. To determine the privacy angle, calculate the variance of each attribute. For each pair of attribute, minimum varia nce will be considered as privacy angle. After determining pairwise optimum privacy threshold, select the range of angles to transform the pair of attribute. While choosing the range of angle make sure that it satisfying the following constraint which is mentioned in Eqn(1) Var(Pi-Pi) ≠¥PoptEqn(1) An angle is randomly chosen from the interval to rotate each and every pair of principal component. After rotating the principal component, it is multiplied with normalized data in order to obtain the transformed data. The Transformed value of the original data for the data is shown in Table 2. Private Neighborhood Network Construction Original network is compressed using power graph analysis. Power graph analysis is a representation of complex network which represent the graph into power graph without loss of information. Graph can be clustered to construct a power graph using modular decomposition method in which modules represents the nodes with same neighbour. Power graph cluster both, the nodes and edges to obtain the most compressed network. Power graph analysis is widely used several biological networks such as protein-protein interactionnetworks, domain-peptide binding motifs,Gene regulatory network and Homology/Paralogy networks. Matrix R can be used to represent the social relationship between the users. If any two users rate the same item then there will be a relationship between them. Thus the user-user network is represented as where U is the set of users represented as nodes and is the set of relationship denoted as edges, and then a power graph is a graph defined on the power set of nodes which are connected by power edges. The concepts of power graphs are as follow: if there is a power edge between two power nodes, then nodes in one power nodes are connected to all the nodes in the second power node. In same way if all the nodes are connected to each other which is represented as power node with self loop. Based on power graph analysis this module involves two steps, power node identification and power edge search. Power nodes are recognized using hierarchical clustering based on jaccard index. The greedy search is performed to identify the power edge. Feature Set Extraction After construction of private network, feature set of each user is extracted by categorizing the users into cold start user, powerful user, and malicious user. Cold start is a user who rate exactly twenty items. Powerful user is user who rate more than thirty five items and malicious user who rate less than twenty five items but the difference between any ratings of a particular item and the standard deviation of that item is greater than one. For the constructed compressed network following features are extracted for each category of the users to predict the rating of unrated item. Feature set of particular user includes features of directly connected power node and Friend Of A Friend (FOAF) in the other power nodes. Each category is measured according to number of particular category of user present in the power node and their joint probability of that particular category. Bayes theorem is used to calculate the most probable rating each category of user. The following Table shows t he feature set measured for each user. Table 5.2 Feature Set of User X Linear Regression prediction From the above extracted features a linear predictive model is constructed which is user for predicting particular item. Then top predicted items are given as recommendation to user. The model takes the following form as in Equation. (5.6) (5.6) where ÃŽ ± represents the slope of the dependent variables, X represent the feature vectors and represents the error vector which is assumed to be zero. In linear regression, the value to be predicted is commonly computed from the best fitting line which reduces error in prediction. PERFORMANCE ANALYSIS: The proposed method is preserves both the individual neighbors privacy and data privacy. It also reduces the scalability issues and give accurate recommendation when compared to the previous work (privacy preserving information filtering system). MAE obtained is compared with the proposed method in previous Chapter TRPC as in Table 5.7. Figure shows that MAE is reduced to 0.62 because of coupling of clustering approach with data transformation to handle large volume of data. CONCLUSION The power graph analysis helps to overcome the scalability problem by compressing the original network and results better recommendation to users. The existing methods apply power graph analysis to various domains for analyzing complex networks in a simpler way. And at the same time it also preserves the communities’ information. Therefore, in proposed work this type of clustering approach is used to preserve the neighbours information which also results better prediction. The efficiency of the proposed methodology is evaluated with the experimental results using MovieLens dataset. It performs better compared data transformation and clustering approach. This type of cluster based collaborative filtering recommendation helps to reduce the edges in the original network without loss of any information.

How P&G Brought the Diaper Revolution to China Essay

When Procter & Gamble set out to sell Pampers in China more than a decade ago, it faced a daunting marketing challenge: P&G didn’t just have to persuade parents that its diapers were the best. It had to persuade many of them that they needed diapers at all. The disposable diaper — a throwaway commodity in the West — just wasn’t part of the cultural norm in the Chinese nursery. Babies wore cloth diapers, or in many cases, no diaper at all. And that, says Bruce Brown, who’s in charge of P&G’s $2 billion R&D budget, is why China presented — and still presents — such a huge opportunity. Today, after years of exhaustive research and plenty of missteps, Pampers is the No. 1-selling diaper in China and the company, in many ways, is just getting started there. The diaper market in China is booming. It stands at $1. 4 billion — roughly a quarter the size of the U. S. market — and is projected to grow 40 percent over the next few years, according to research firm Datamonitor. P&G’s success in China has helped CEO Bob McDonald set some bold goals. Last October, he laid out a plan to add one billion customers over the next five years by promoting P&G brands throughout some of the poorest corners of the world. How will P&G go about doing that? To get a sense, just look at the way it cracked — and to a large degree created — the market for disposable diapers in China. Learning From Failure When P&G first launched Pampers in China in 1998, the effort flopped. Instead of developing a unique product for the market, P&G made a lower-quality version of U. S. and European diapers, wrongly assuming that parents would buy them if they were cheap enough. â€Å"It just didn’t work,† Brown says. Chinese split-pants, or kaidangku. Photo by The Wu’s Photo Land on Flickr It didn’t help that Chinese families had always gotten along just fine without disposable diapers. There, potty training often begins as early as six months, and children wear what’s called kaidangku — colorful open-crotch pants that let them squat and relieve themselves in open areas. Pampers’ pitch wasn’t compelling people to try something new — and neither was the product itself. â€Å"We scrimped on the softness in the earlier versions,† says Kelly Anchrum, director of global baby care, external relations, and sustainability. â€Å"It had a more plasticky feel. It took us awhile to figure out that softness was just as important to moms in a developing market. † P&G had tried a similarly watered-down approach earlier in the decade, when it launched laundry and hair-care brands in several emerging markets. Those products also failed, Brown says. After these experiences, the company in 2001 came up with a new approach to product development: â€Å"Delight, don’t dilute. † In other words, the diaper needed to be cheap, but it also had to do what other cheap diapers didn’t — keep a baby dry for 10 hours and be as comfortable as cloth. So P&G added softness, dialed down the plastic feel, and increased the absorption capability of the diaper. To bring down the cost, the company developed more efficient technology platforms and moved manufacturing operations to China to eliminate shipping costs. The revamped diaper, Pampers Cloth Like & Dry, hit retail shelves in China’s largest cities in 2006, selling for the equivalent of 10 cents in local currency, less than half the cost of a Pampers diaper in the United States. The Universal Pitch P&G had the right diaper and the right price point. Now it faced the bigger challenge. â€Å"You have to convince someone that they need this thing,† says Ali Dibadj, an analyst who covers P&G at Sanford C. Bernstein & Co. For Frances Roberts, global brand franchise leader for Pampers, every trip to China was (and still is) an opportunity to learn more about Chinese nursery habits. It’s part of the P&G ethos that brand leaders visit consumers in their own homes — something Roberts has done in dozens of countries, including Germany, Russia, and Jakarta. The goal is to uncover the nuances of each market, and early on in its diaper research P&G discovered a universal need. â€Å"Moms say the same things over and over,† Roberts says. Their cry: We want more sleep. With the help of the Beijing Children’s Hospital’s Sleep Research Center, P&G researchers conducted two exhaustive studies between 2005 and 2006, involving 6,800 home visits, and more than 1,000 babies throughout eight cities in China. Instead of cloth, the research subjects were tucked into bed with Pampers. The results: P&G reported that the babies who wore the disposables fell asleep 30 percent faster and slept an extra 30 minutes every night. The study even linked the extra sleep to improved cognitive development, a compelling point in a society obsessed with academic achievement. P&G then put its marketing machine into motion. Pampers launched the â€Å"Golden Sleep† campaign in 2007, which included mass carnivals and in-store campaigns in China’s biggest urban areas. A viral campaign on the Pampers Chinese web site asked parents to upload photos of their sleeping babies to drive home the study’s sleep message. The response was impressive: 200,000 photos, which P&G used to create a 660-square-meter photomontage at a retail store in Shanghai. The ad campaign boasted â€Å"scientific† results, such as â€Å"Baby Sleeps with 50% Less Disruption† and â€Å"Baby Falls Asleep 30% Faster. No diaper brand, not even rival Kimberly-Clark, maker of Huggies, has come close to spending as much on advertising in China, according to CTR Market Research, the China-based division of American media researcher TNS Media Intelligence. Since 2006, Pampers’ measured media spend topped 3. 2 billion yuan, or about $476 million — more than three times as much as any other brand. In 2009 alone, P&G spent $69 million, compared to Kimberly-Clark’s $12 million spend for Huggies. Ruling the Nursery — in China and Around the World Today, Pampers is the top-selling brand in China, a country where about a decade ago the disposable diaper category hardly existed. P&G does not release sales figures for specific countries, but Datamonitor estimates that the company has captured more than 30 percent of the $1. 4 billion market. Karl Gerth, an Oxford professor who researches the spread of consumerism in China, says P&G’s marketing campaigns strike the right tone. â€Å"You don’t want to come off as paternalistic,† says Gerth, who wrote the book â€Å"China Made: Consumer Culture and the Creation of the Nation. â€Å"The idea that Pampers brings a scientific backing and gives children an edge in their environment — that’s a brilliant way to stand out from the competition. † You could argue that it’s easy being No. 1 when the market is still small. But P&G still has a lot of work to do. The company faces challenges from private-label and domestic brands, including the No. 2 market leader, Hengan International Group, which has steadily grown its market share to 20 percent. Local brands, meantime, are catching up with better products, marketing, and distribution. Chinese consumers are going to want to root for the home team,† Gerth says. And there’s still the challenge of making disposables a habit. On average, diaper use still amounts to less than one a day. â€Å"We’ve only just begun to scratch the surface [in China],† Dimitri Panayotopoulos, vice chairman of global household care, told investors in a 2008 analyst meeting. There’s even bigger potential in India, where the birth rate is almost double that of China but the diaper market remains tiny at about $43. 4 million. (Pampers is the top-selling brand there, too. So now, P&G plans to take the sleep argument throughout rural and poor areas in India and elsewhere. The company also makes its case by positioning itself as a baby-care educator. Pampers sponsors healthcare-outreach programs such as a rural immunization program in China and mobile medical-care vans in Pakistan and Morocco. In India, there’s a door-to-door program that offers baby-care tips and diaper samples for moms. Of course, P&G tweaks the sales pitch to fit different markets; that’s what the company is known for. In India, for instance, the convenience of disposable diapers doesn’t resonate with parents. The company’s consumer research found that many Indian mothers think that only lazy moms put their babies in disposable diapers that last a full night. As Pampers brand manager Vidya Ramachandran reported in an internal video shown to employees, â€Å"We really had to change that mindset and educate [mothers] that using a diaper is not about convenience for you — it’s about your baby’s development. †

Huawei Suicide Scandals

Huawei Suicide Scandals Songyan Liang Harding University Abstract Nowadays, Huawei get worldwide people’s eyes since it become the second-largest integrated provider of telecommunications equipment in the world since 2009. It is hardly to make people to believe this is true. Although Huawei is successful, four suicide scandals drive people’s attention to wonder Huawei Company management. This article is focusing on Huawei cultural background: â€Å"wolf culture† and human resource management. Huawei Suicide ScandalsNowadays, Huawei get worldwide people’s eyes since it become the second-largest integrated provider of telecommunications equipment in the world since 2009(Economist, 2009). It was listed in the top of Intellectual Property Organization’s 2008 ranking for international Patent applications (Economist, 2009). It is hardly to make people to believe this is true. Although Huawei is successful, four suicide scandals drive people’s atten tion to wonder Huawei Company management. This article is focusing on Huawei cultural background: â€Å"wolf culture† and human resource management. Introduction Huawei Technologies Co. , Ltd. which was founded in 1987 in Shenzhen, ranked first in the 2011 top 500 Chinese private companies list in the November 8, 2011. In the famous American magazine â€Å"Fortune†, Huawei was listed in the 500 companies rankings in 2010 (Wiki, 2011). It headquarters in Shenzhen, Guangdong, China, and it is an employee-owned private technology company which engages in the production and sales of telecommunications equipment. Furthermore, it is a telecommunications network solutions provider. Huawei's main businesses are exchange, transmission, wireless and data communications class telecommunications products (Wiki, 2011).In the field of telecommunications, it can provide worldwide customers network equipment, services and solutions. Since the president Ren founded Huawei in 1987, he p romoted an aggressive â€Å"wolf culture† throughout the company (Beijing Review, 2007). The â€Å"wolf culture† is a kind of struggle with a wild spirit. In the team, all employees should be greed to work. This culture can quote employee to fight and explore the endless future. Also, as a result of Ren is a veteran, he worships militarization management. He tries to cultivate employees’ never give up spirit and build an effective team.In order to consistently create maximum value for worldwide customers, Huawei focuses on customers’ marker challenges and needs by providing great ICT solutions and services (Huawei, 2011). Ethics Background Huawei keeps a promise that it will enrich customers’ lives as possible as it can through communication. Core values are very important for a successful company. For Huawei, customers first is the most important value. Huawei believes that its success is based on customers’ success. It continuously create core value for fulfill customers’ needs and requirements. The second core value is dedication.Through dedication, Huawei can get customers’ respect and trust. Continuous improvement is the third core value. It requires Huawei to become the best partners for worldwide customer, improve company and grow as individuals (Huawei, 2011). Fourth, openness and imitative. In order to fulfill customers’ needs, Huawei, keeping an open manner, passionately pursue customercnetric innovations. Fifth, Huawei insists that integrity is the most valuable asset for its success. It drives Huawei to be honest and keep promises. At the same time, integrity helps Huawei to win customers’ trust and respect. The last core value is teamwork.Without team, Huawei cannot success. The president Ren said that Huawei’s success is built on successful cross-cultural collaboration, streamlined inter-departmental cooperation and efficient process. Every year Huawei will recruit a lar ge number of graduates from colleges and universities. Most of them are lack of practical experience in software development. Therefore, Huawei develops a new training system for new employees in order to help graduates quickly adapt to the scale of software development. This training system is divided into four parts. First, process development training. Second, programming-based training.Third, business knowledge training. Fourth, positive defense training. Besides, Huawei establishes a series of IP certifications system. In the rapidly growing trend of All-IP convergence environment, increasing numbers of people are extensively applying IP technologies. In the globalization, IP technical competence is a basic factor to improve individual employment rate and personnel’s competence. In response to customers’ needs, Huawei developed the Huawei Certifications System to help employees to assess IP expertise and develop essential skills. It provides three kinds of career certification (Huawei, 2011).The first one is Huawei Certified Datacom Associate. The second one is Huawei Certified Datecom Professional. The last one is Huawei Certified Datecom Expert. Employees’ Suicide Scandal March 6th, 2008 at noon, a Huawei staff who worked in Shenzhen Huawei R base Sakata Department of the Central platform died after falling on the third floor cafeteria. Police announced that the jumping suicide employee had insanity disease before his death. 10 days before the suicide scandal, February 26th, Dongbing Li, who worked in Chengdu, jump from Huawei R center and died on the spot.At 17:30 on August 11, 2007, in Changchun City, a Huawei staff, Bin Zhao, who worked in Changchun office, jumped from the 7th floor in the League of Nations communities after about 20minutes phone conversation. July 18, 2007 afternoon, only 26years old Huawei employee, Yue Zhang, hanged himself in the corridor area in Shenzhen Melin communities. He just entered into Huawei for 60 days. Before his death, he told to his parents several times and complained that he suffered endless pressure and wanted to resign.From 2007 to 2008, as a result of four employees’ suicide in less than a year, Huawei had undoubtedly put himself in the predicament of staff stress management. Fifteen minutes after Dongbing Li’s death, Tianya forum posted a great deal of messages and photos to describe the scene. According to scene photos, some posters were Huawei employees. They carried out real-time updated on this matter. It provided on-site users the latest developments. Various QQ groups which related to media and IT industry expressed a high degree attention to it immediately.At the same time, some on-site users described various kinds of aspects of this matter when they were in the scene. Some people believed that this suicide was result from corporate culture; some argued that whether the suicide was worth or not. In the Tianya forum, more than 2,000 on-site users participated in this investigation. Most of user contended that the basically reason for suicide was Huawei’s â€Å"wolf culture†. Huawei, once again, was brought to the cusp of public opinion. The company spokesperson explained that the company would not temporarily comment whether this suicide was related to work stress or not.Suicide Analysis After Dongbing Li’s death, the company once again became the focus of public opinion. â€Å"Cruel†, â€Å"competitive†, â€Å"tremendous pressure†, â€Å"overtime†, â€Å"Human Resource Management†, â€Å"ethic issues† or similar words, became heated words in the IT industry. Huawei’s human resources model was the one of reasons for employees’ suicide (QQ News, 2008). According to an unnamed Huawei older employee explored, Huawei human resources management was outsourced a private company which was Human resource management (QQ News, 2008). If Huawei needed a new e mployee, it would contract with the human resources firstly.According to Huawei human resources needs, the Human Resources Company would provide relevant labor force to Huawei. This model was called dispatch. Therefore, Huawei’s employees were sent to Huawei Company to work by Human Resource Company. Huawei paid compensation to the Human Resources Company, so employees get the money from the Human Resource Company. Staffs had not a direct relationship to human resource management. The Human Resource Company was in charge of labor management. This model reduced Huawei human resources management costs and avoided the risk of the employment law.As a result of China’s labor sub-system still needs to sound, employees’ basic rights do not be protected. They face a series of issues, and they will lose their jobs at any time (QQ News, 2008). The same as the most Chinese company leaders, Huawei leaders would like to find the law loopholes and maximize the use these looph oles these law loopholes. In the China business world, the character of integrity is becoming unfamiliar gradually. Based on Huawei employees’ frequent non-normal deaths, Huawei president Ren acknowledged that they did not take some measures to prevent employees’ suicide.He worried that growing numbers of employees were suffering depression and anxiety. When coming to corporate responsibility, Huawei should be responsible for physical and psychological safety of employees because the company was staffs’ work place and it was the place where produce stress. Some companies did well in employees’ physical and psychological safety, such as Haier, Lenovo. They would provide psychological counseling to staffs and evaluated staffs. The psychological evaluation was used to check whether staffs suffered excessive pressure, mental illness or other issues.Another factor lead employees’ suicide was Huawei’s â€Å"wolf culture†. There is no denyin g that wolf culture is an advanced corporate culture. The wolf culture not only focuses on teamwork, but also is in process of the spirit of never give up and never abandon. Meanwhile, wolf culture has the courage to overcome difficulties. The wolf culture can be divided into two parts: external and internal. In addition to business can use wolf culture to deal with competitive pressure from external environment, the wolf culture play an essential role in the internal management (Baidu, 2011).For example, wolf culture emphasizes the collaborative and discipline. The wolf culture is a sharp-edged sword. Behind the wolf culture, wolf has a deep inherent nature of ruthless (Baidu, 2011). In order to achieve the purpose, people will any unscrupulous measures. It is easily lead companies and employees to lose their culture. The wolf culture highlights the extreme relationship and emphasis on competition among people (Baidu, 2011). In the enterprise’s internal management, it is eas y to form a strong rigid culture due to employees lose their humanity. The wolf culture can be the internal factor for Huawei employees’ suicide.Employees could not endure the high competition in the workplace. The wolf culture made employees produce the mutual suspicion and no trust sense. Between employees and employers and among employees, they hold an â€Å"evil nature of human† principle. Solution After a series of suicides scandals, Huawei realized that they should intervene human resource management. In order to deal with the external pressure, Huawei tried to reduce and eliminate the external inappropriate management and environmental factors. For example, Huawei organized some entertainment programs during weekend.Besides, Huawei tired to establish a psychological evaluation system to release staffs’ pressure. Summary Even though Huawei has become the second-largest integrated provider of telecommunications equipment in the world since 2009, four suicid e scandals did not change its Huawei human resource management and wolf culture. Huawei is quite different from other international enterprises. It was founded on the Chinese culture. Also, China law system is not as perfect as the Unite States. All of these factors lead employees to enter into the suicide road. Reference Baidu (2011), wolf culture, retrieved from: http://baike. aidu. com/view/155988. htm Beijing Review (2007), Tech Boss Slammed for Staff Layoffs. Vol. 50 Issue 46, p3-3, 2/3p, 1 Color Photograph Economist (2009), Up,up and Huawei, Vol. 392 Issue 8650, special section p13-15, 3p Huawei (2011), Vision ; Mission, Retrieved from: http://www. huawei. com/en/about-huawei/corporate-info/vision-mission/index. htm QQ News (2008,Feb 27), Huawei: temporarily comment whether employees’ suicides related to work stress, Retrieved from: http://news. qq. com/a/20080227/001389. htm Wikipedia (2011), Huawei, Retrieved from: http://en. wikipedia. org/wiki/Huawei

Shakespeare Sonnet Compare and Contrast Essay - 777 Words

Sonnet Compare and Contrast Essay Love can be expressed and described in many different ways. Shakespeare`s sonnets â€Å"116† and â€Å"18† justify that love has the ability to create extremely powerful feelings between two people, which can help them achieve the ultimate sense of happiness. To that end, when people experience true love they live a more joyful and content life. When a person finds love their lives are filled with joy and pleasure that bring true happiness into ones life. In sonnet â€Å"116†, Shakespeare writes that love should be; â€Å"an ever fixed mark, / That looks on tempests and is never shaken,† (lines 4-5). Shakespeare is speaking of a building that could never be destroyed. This quote carries a metaphor within it, by†¦show more content†¦By using an association with something tangible like the warmth and beauty of summer, Shakespeare is able to validate the couples enthusiasm and intensity of feelings for each other. As shown in sonnet â€Å"116† metaphors are also used to show love, Lines 4-5 provide an even deeper and more significant meaning for this sonnet. The metaphor makes love seem like an unbreakable structure, one that is built on trust, admiration, and no doubt of wavering feelings. Lines 4-5 contain examples of diction such as, â€Å"never,† â€Å"shaken,† and â€Å"tempestà ¢â‚¬ . Diction is used in this quote to create a deeper meaning and emphasize on how firm the structure should be. The word â€Å"never† gives a sense of confidence that nothing will ever be destroyed and the love will always be the same. Love creates enthusiasm within a person, causing them feel a deep compassion for another, but love also makes a person stronger by guiding them through problematic moments Love should be able to guide people through hard times and support them when they need it most. The quote in line 2-5 of Sonnet â€Å"116† is very metaphorical because the quote implies that love should be strong and will never let a person down no matter how hard the situation is. As Shakespeare said, â€Å"Love is not love, Which alters when it alteration finds, Or bends with the remover to remove,† he refers to love as being something tangible, as a solid unbreakable object. ByShow MoreRelatedThe Movement Of The 18th And 17th Century Essay1526 Words   |  7 PagesBeat poets (20th Century). These movements have been fundamental to change the course of poetry in and out of their eras. This essay will be a critical literature review of the movement in the 16th and 17th century, the period after the Italian Renaissance waned. The essay will look into the hallmarks of this period; Sonnets and their incredible movement into Europe. The essay will underscore that indeed poets are not solitary, hermetic personalities but are an expression of the communities. 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